removed lines

lm_eval/api/instance.py

@@ -6,8 +6,6 @@ OutputType = Literal[
     "loglikelihood", "loglikelihood_rolling", "generate_until", "multiple_choice"
 ]
 
-InputType = Literal["text", "text_image"]
-
 
 @dataclass
 class Instance:
@@ -16,9 +14,6 @@ class Instance:
     arguments: tuple
     idx: int
 
-    # Input type for multimodal
-    input_type: InputType = "text"
-
     metadata: Tuple[Optional[str], Optional[int], Optional[int]] = field(
         default_factory=lambda: (None, None, None)
     )

lm_eval/api/model.py

@@ -36,7 +36,7 @@ class LM(abc.ABC):
         LM calls whenever possible.
 
         :param requests: list[Instance]
-            A list of Instance objects, with property `args` which returns a tuple (context, continuation, visual_list).
+            A list of Instance objects, with property `args` which returns a tuple (context, continuation).
             `context: str`
                 Context string. Implementations of LM must be able to handle an
                 empty context string.
@@ -44,8 +44,6 @@ class LM(abc.ABC):
                 The continuation over which log likelihood will be calculated. If
                 there is a word boundary, the space should be in the continuation.
                 For example, context="hello" continuation=" world" is correct.
-            'visual_list: Optional[list[dict]]'
-                Visual Input to the model. Can be None
 
         :return: list[tuple[float, bool]]
             A list of pairs (logprob, isgreedy)

lm_eval/models/llava.py

@@ -325,9 +325,7 @@ class Llava(LM):
         pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding")
         for chunk in chunks:
             contexts, all_gen_kwargs, visuals = zip(*chunk)
-            # task = task[0]
 
-            # visuals = [doc_to_visual[0](doc[0])]
             visuals = self.flatten(visuals)
             # we assume all gen kwargs in the batch are the same
             # this is safe to assume because the `grouper` object ensures it.